Method and system for estimating a posteriori a number of persons in one or more crowds by means of aggregated data of a telecommunication network

ABSTRACT

A method of estimating a number of persons gathered at an Area of Interest during an observation time interval on a day, wherein the Area of Interest is covered by a mobile telecommunication network including plural communication stations each of which is configured to manage communications of User Equipment in one or more served areas in a covered geographic region over which the mobile telecommunication network provide services.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to crowd counting, i.e. to techniques forcounting or estimating the number of persons in a crowd. In the presentdescription and for the purposes of the present invention, by “crowd” itis meant a gathering of a certain number of people, gathered in acertain location for, e.g., attending at public events or happenings, ofthe most disparate nature, like for example (and non-exhaustively) livetelevision shows, artistic/entertaining performances, culturalexhibitions, theatrical plays, sports contests, concerts, movies,demonstrations and/or for visiting a place of particular interest suchas for example a museum, a monument, a building, and so forth.

Particularly, the present invention relates to crowd counting techniquesexploiting information provided by wireless or mobile telecommunicationnetworks.

OVERVIEW OF THE RELATED ART

In the tasks of urban planning, management of activities (e.g.,transport systems management and emergencies management), and tourismand local marketing, it is useful to have a knowledge of amounts ofpeople who gathered at certain locations or Areas of Interest (AoI forshort, e.g., a building, such as for example a stadium or a theatre or acinema, the surroundings thereof, a square or a street(s) of a city ortown or village, a district etc.), e.g. because they attended at publichappenings like shows (e.g., related to culture, entertaining, politicsor sports) that took place within the Area of Interest and/or forvisiting a place of interest (also denoted as point of interest) withinthe Area of Interest.

In case of one or more gatherings of people related to publichappenings, although the following considerations apply to gatherings ofpeople related to points of interest as well, this knowledge allows forexample a more effective planning of subsequent public happenings of thesame type. Particularly, this knowledge allows a more effective planningand managing of resources and activities (such as infrastructures,transport system and security) directly or indirectly related to similarpublic happenings that may take place in the future (such as for examplesports matches that regularly take place at a stadium). Moreover, from acommercial viewpoint, this knowledge allows a better management ofmarketing activities intended to promote similar events that may takeplace in the future.

Nowadays, mobile communication devices (referred to as mobile phones orUE in the following, including cellular phones, smartphones, tablets andthe like) have reached a thorough diffusion among the population of manycountries, and mobile phone owners almost always carry their mobilephones with them. Since mobile phones communicate with a plurality ofbase stations of the mobile phone networks, and each base station covers(i.e., serves) one or more predetermined serving areas, or cells, whichare known to the mobile communication services provider (e.g. mobilephone network owner or virtual mobile phone services provider), mobilephones result to be optimal candidates as tracking devices forcollecting data useful for identifying the amount of people who attendedto one or more public happenings.

In the art, many systems and methods have been proposed in order tocollect information about time and locations at, and in which, a UserEquipment (UE, e.g. a mobile phone, a smartphone, a tablet, etc.) of anindividual connects to the mobile phone network (e.g., for performing avoice call or sending a text message), and use such collectedinformation in order to derive information related to how many attendeesa certain public happening had.

For example, F. Calabrese, F. C. Pereira, G. Di Lorenzo, L. Liu, C.Ratti, “The Geography of Taste: Analyzing Cell-Phone Mobility in SocialEvents,” Pervasive Computing, LNCS 6030, Springer, 2010, pp. 22-37,discloses the analysis of crowd mobility during special events. Nearly 1million cell-phone traces have been analyzed and associated with theirdestinations with social events. It has been observed that the originsof people attending an event are strongly correlated to the type ofevent, with implications in city management, since the knowledge ofadditive flows can be a critical information on which to take decisionsabout events management and congestion mitigation.

Traag, V. A.; Browet, A.; Calabrese, F.; Morlot, F., “Social EventDetection in Massive Mobile Phone Data Using Probabilistic LocationInference”, 2011 IEEE Third International Conference on Privacy,Security, Risk and Trust (Passat), and 2011 IEEE Third InternationalConference on Social Computing (Socialcom), pp. 625, 628, 9-11 Oct.2011, focuses on unusually large gatherings of people, i.e. unusualsocial events. The methodology of detecting such social events inmassive mobile phone data is introduced, based on a Bayesian locationinference framework. More specifically, a framework for deciding who isattending an event is also developed. The method on a few examples isdemonstrated. Finally, some possible future approaches for eventdetection, and some possible analyses of the detected social events arediscussed.

Francesco Calabrese, Carlo Ratti, “Real Time Rome”, Networks andCommunications Studies 20(3-4), pages 247-258, 2006, discloses the RealTime Rome project, presented at the 10th International ArchitectureExhibition in Venice, Italy. The Real Time Rome project is the firstexample of a urban-wide real-time monitoring system that collects andprocesses data provided by telecommunications networks andtransportation systems in order to understand patterns of daily life inRome. Observing the real-time daily life in a town becomes a means tounderstanding the present and anticipating the future urban environment.

F. Manfredini, P. Pucci, P. Secchi, P. Tagliolato, S. Vantini, V.Vitelli, “Treelet decomposition of mobile phone data for deriving cityusage and mobility pattern in the Milan urban region”, MOX—Report No.25/2012, MOX, Department of Mathematics “F. Brioschi”, Politecnico diMilano, available at http://mox.polimi.it, discloses a geo-statisticalunsupervised learning technique aimed at identifying useful informationon hidden patterns of mobile phone use. These hidden patterns regarddifferent usages of the city in time and in space which are related toindividual mobility, outlining the potential of this technology for theurban planning community. The methodology allows obtaining a referencebasis that reports the specific effect of some activities on the Erlangdata recorded and a set of maps showing the contribution of eachactivity to the local Erlang signal. Results being significant forexplaining specific mobility and city usages patterns (commuting,nightly activities, distribution of residences, non systematic mobility)have been selected and their significance and their interpretation froma urban analysis and planning perspective at the Milan urban regionscale has been tested.

Ramon Caceres, James Rowland, Christopher Small, and Simon Urbanek,“Exploring the Use of Urban Greenspace through Cellular NetworkActivity”, 2nd Workshop on Pervasive Urban Applications (PURBA), June2012, discloses the use of anonymous records of cellular networkactivity to study the spatiotemporal patterns of human density in anurban area. This paper presents the vision and some early results ofthis effort. Firstly, a dataset of six months of activity in the NewYork metropolitan area is described. Secondly, a technique forestimating network coverage areas is presented. Thirdly, the usedapproach in analyzing changes in activity volumes within those areas isdescribed. Finally, preliminary results regarding changes in humandensity around Central Park are presented.

SUMMARY OF THE INVENTION

The Applicant has observed that, generally, method and systems known inthe art provide unsatisfactory results, as they are not able todetermine (or have a limited ability in determining) whether a UE ownerhas been in an Area of Interest (AoI) where one or more publichappenings have been held, for attending thereat or for other reasons(for example, because the UE owner resides or has a business inproximity of, or within, the area of interest). In addition, the resultsprovided by the known solutions are strongly influenced by the size ofthe area of interest selected for the analysis of the amount ofattendees at the one or more public happenings. In other words, if thearea of interest has a large size, a certain number of UE owners thatare not actually part of the crowd will be taken into account in theevaluation of the number of people in the crowd. Conversely, if the areaof interest has small size, a certain number of UE owners actually partof the crowd will be excluded from the evaluation of the number ofpersons in the crowd.

Therefore, subsequent planning and managing of resources and activities(of the type mentioned above) based on results obtained by the methodsand systems known in the art will achieve a limited efficiency due tothe limited accuracy thereof.

Moreover, known method and systems based on the use of informationregarding positions occupied by each UE while connected to the mobilephone network (information that are collected by mobile phone networksserving the UE) could be intrusive of a privacy of the owners of the UE.

Indeed, such information allow knowing habits, routines of, and places(e.g., home and work places) daily frequented by, the UE owners.

Accordingly, the use of such information is thus usually highlyrestricted (to the extent of being prohibited) by laws issued by manyNational Authorities in order to protect the privacy of the UE owners.

In this respect, “anonymization” techniques known in the art and usedfor anonymizing information about the UE owners, in order to circumventprivacy issues, do not grant a satisfactory protection of the privacythereof.

Generally, the anonymization techniques comprise masking any identifiers(such as for example an International Mobile Equipment Identity—IMEI, anInternational Mobile Subscriber Identity—IMSI, or a Mobile SubscriberISDN Number) associated with the UE and/or the UE owner with encipheredidentifiers.

Nevertheless, an analysis of the information collected over a pluralityof days may be intrusive of UE owners since it anyways allowsidentifying sensitive information regarding habits, home and work placesof the UE owners and, possibly, the UE owners themselves by analyzingsuch sensitive information so obtained.

The Applicant has thus coped with the problem of devising a system andmethod adapted to overcome the problems affecting the prior artsolutions.

The Applicant has found that it is possible to determine the size of anoptimal area of interest on the basis of data related to the UE duringthe course of the one or more public happening and in a certain numberof days preceding the one or more public happening. Moreover, theApplicant has found that it is possible to protect the privacy of the UEowners by exploiting aggregated data regarding the usage of at least onemobile phone network.

For example, aggregated data exploitable by the present inventioncomprise a number of UE served by the mobile phone network within one ormore time interval (i.e., no information about single UE is providedthat may infringe upon UE owner privacy).

Preferably, by using aggregated data regarding separately one or moreserved areas of the mobile phone network it is possible to determine thesize of an optimal area of interest and then a number of people thatgathered within it with a high precision.

It should be noted that the knowledge of the number of UE served by themobile phone network within one or more time interval in the optimalarea generally does not correspond to the number of people in the crowd.Indeed, the number of UE served by the mobile phone network within oneor more time interval in the optimal area comprise UE owned by people inthe optimal area for reasons (e.g., work, people simply passing by)other than gathering in the crowd.

The Applicant has further found that it is possible to discern thenumber of people in the crowd within the optimal area from people thatare in the optimal area but are not in the crowd on the basis of theanalysis of aggregated data referred to the mobile phone network usageduring the gathering and during a number of days previous to the day inwhich the gathering of people occurred.

Particularly, one aspect of the present invention proposes a method ofestimating a number of persons An that gathered at an Area of Interestduring an observation time interval [Tsn, Ten] on a day gn, wherein saidArea of Interest is defined by an Area of Interest center C and an Areaof Interest radius Ra and is covered by a mobile telecommunicationnetwork having a plurality of communication stations each of which isadapted to manage communications of User Equipment in one or more servedareas in a covered geographic region over which the mobiletelecommunication network provides services. The method comprising thesteps of: a) defining plurality of calculated radius values Rk of theArea of Interest radius, and, for each calculated radius value: b)computing a first number Unk of User Equipment that has been served bythe mobile communication network during the observation time interval[Tsn, Ten] on the day gn within the Area of Interest based on aggregateddata u_(q,t) regarding a usage of the mobile communication network; c)computing a second number Upnk of User Equipment that has been served bythe mobile communication network during the observation time interval[Tsn, Ten] for each day gpn of a predetermined number P of previous dayspreceding the day within the Area of Interest based on the aggregateddata u_(q,t) regarding the usage of the mobile communication network; d)combining the first number Unk of User Equipment and the second numbersUpnk of User Equipment for obtaining a statistical quantity Znk; e)detecting the occurrence of a gathering of people if the statisticalquantity Znk reaches a certain threshold Zth; f) computing an optimumradius value Ro of the Area of Interest radius Ra as the average of thecalculated radius values Rk within which the gathering of people isdetected; g) estimating the number of persons An that gathered within anArea of Interest having the Area of Interest radius Rs equal to theoptimum radius value Ro.

Preferred features of the present invention are set forth in thedependent claims.

In one embodiment of the present invention, the aggregated data u_(q,t)regarding a usage of the mobile communication network comprise a numberof served User Equipment traffic load, number of voice calls, number ofSMS transmitted and/or volume of binary data exchanged within preferablyeach one of the communication stations of the mobile communicationnetwork.

In one embodiment of the present invention, the method further comprisesfor each calculated radius value: i) subdividing the covered geographicregion in a plurality of surface elements, and j) receiving a pluralityof aggregated data u_(q,t) regarding a usage of the mobile communicationnetwork referred to each one of said surface elements.

In one embodiment of the present invention, the step j) of receiving aplurality of aggregated data regarding a usage of the mobilecommunication network for each one of said surface elements, comprisesreceiving a set {u_(q,t)} of aggregated data, each aggregated datau_(q,t) of the set {u_(q,t)} of the aggregated data being referred to arespective reference time interval dr which is a portion of anacquisition period T during which aggregated data u_(q,t) are collected.In one embodiment of the invention, the step b) of computing a firstnumber Unk of User Equipment that has been served by the mobilecommunication network during the observation time interval [Tsn, Ten] onthe day gn within the Area of Interest based on aggregated data u_(q,t)regarding a usage of the mobile communication network, comprisescomputing a first number Unk of User Equipment on the basis of sets{u_(q,t)} of aggregated data referred to respective reference timeintervals dr comprised within the observation time interval [Tsn, Ten]on the day gn, and wherein the step c) of computing a second number Upnkof User Equipment that has been served by the mobile communicationnetwork during the observation time interval [Tsn, Ten] for each day gpnof a predetermined number P of previous days preceding the day gn withinthe Area of Interest based on the aggregated data u_(q,t) regarding theusage of the mobile communication network, comprises computing eachsecond number Unpk of User Equipment on the basis of sets {u_(q,t)} ofaggregated data u_(q,t) referred to respective reference time intervalsdr comprised within the observation time interval [Tsn, Ten] of therespective previous day gpn of the predetermined number P of previousdays preceding the day gn.

In one embodiment of the invention, the first number Unk of UserEquipment and/or each second number Upnk of User Equipment may becomputed as total number, an average number, or a maximum number of UserEquipment in the relevant surface elements comprised in the Area ofInterest.

In one embodiment of the present invention, the method further comprisesthe step of k) identifying a number of relevant surface elements amongthe plurality of surface elements, wherein said relevant surfaceelements are at least partially superimposed on the Area of Interest.

In one embodiment of the invention, a surface element is identified as arelevant surface element if it verifies the following condition:

Dist(C,B)≤|Rs+Rk|,

where C is the center of the Area of Interest, B is the center of theserved surface element, Dist(C, B) is the geographical distance betweenthe center of the Area of Interest C and the center of the surfaceelement B, Rs is the radius of the surface element, and Rk is thecalculated radius value.

In one embodiment of the present invention, the step d) of combining thefirst number Unk of User Equipment and the second numbers Upnk of UserEquipment for obtaining a statistical quantity Znk comprises: combiningthe second numbers Upnk of User Equipment of each one of the previousdays gpn in order to determine an average User Equipment number μnk anda User Equipment number standard deviation σnk.

In one embodiment of the present invention, the step d) of combining thefirst number Unk of User Equipment and the second numbers Upnk of UserEquipment for obtaining a statistical quantity Znk further comprises:computing the statistical quantity as:

Znk=(Unk−μnk)/σnk,

wherein Unk is the first User Equipment number, μnk is the average UserEquipment number and σnk is the User Equipment number standarddeviation.

In one embodiment of the present invention, the plurality of calculatedradius values Rk ranges from a minimum radius value Rmin to a maximumradius value Rmax, each calculated radius value being separated from anext calculated radius value by an iteration width Δ.

In one embodiment of the present invention, the step g) of estimatingthe number An of persons that gathered in the Area of Interest havingthe Area of Interest radius Rs equal to the optimum radius value Rocomprises: I) identifying a number of relevant surface elements amongthe surface elements subdividing the covered geographic region, whereinsaid relevant surface elements are surface elements at least partiallysuperimposed on the Area of Interest having the Area of Interest radiusRs equal to the optimum radius value Ro.

In one embodiment of the present invention, a surface element isidentified as a relevant surface element if it verifies the followinginequality:

Dist(C,B)≤|Rs+Ro|,

where C is the center of the Area of Interest (107), B is the center ofthe served area, Dist(C, B) is the geographical distance between thecenter of the Area of Interest C and the center of the surface elementB, Rs is the radius of the surface element, and Ro is the optimum radiusvalue.

In one embodiment of the present invention, the step g) of estimating anumber An of persons that gathered in the Area of Interest having theArea of Interest radius Rs equal to the optimum radius value Ro furthercomprises: computing a third number Un|_(AOI) of User Equipment as anumber of User Equipment comprised within the relevant surface elementscomprised in the Area of Interest having the Area of Interest radius Rsequal to the optimum radius value Ro during the time interval [Tsn, Ten]on the basis of the aggregated data u_(q,t) regarding a usage of themobile communication network.

In one embodiment of the present invention, computing a third UserEquipment number, comprises computing the third number Un|_(AOI) of UserEquipment on the basis of sets {u_(q,t)} of aggregated data referred torespective reference time intervals dr comprised within the observationtime interval [Tsn, Ten] on the day gn.

In one embodiment of the present invention, the step g) of estimating anumber An of persons that gathered in the Area of Interest having theArea of Interest radius equal to the optimum radius value furthercomprises: computing a fourth number Upn|_(AOI) of User Equipment as anumber of User Equipment comprised within the relevant surface elementscomprised in the Area of Interest having the Area of Interest radius Rsequal to the optimum radius value Ro for each day gpn of thepredetermined number P of previous days preceding the day gn on thebasis of the aggregated data u_(q,t) regarding a usage of the mobilecommunication network.

In one embodiment of the present invention, computing a fourth numberUpn|_(AOI) of User Equipment, comprises computing each fourth numberUpn|_(AOI) of User Equipment on the basis of sets {u_(q,t)} ofaggregated data referred to respective reference time intervals drcomprised within the observation time interval [Tsn, Ten] of therespective previous day gpn of the predetermined number P of previousdays preceding the day gn.

In one embodiment of the present invention, the third number Un|_(AOI)of User Equipment and/or each fourth number Upn|_(AOI) of User Equipmentmay be computed as total number, an average number, or a maximum (peak)number of User Equipment in the relevant surface elements comprised inthe Area of Interest having the Area of Interest radius Rs equal to theoptimum radius value Ro.

In one embodiment of the present invention, the step g) of estimating anumber An of persons that gathered in the Area of Interest having theArea of Interest radius Rs equal to the optimum radius value Ro furthercomprises: combining the fourth numbers Upn|_(AOI) of User Equipment ofeach one of the previous days in order to determine a further averageUser Equipment number μn|_(AOI), the further average User Equipmentnumber μn|_(AOI) providing an indication of an average number of personsnormally comprised within the Area of Interest having the Area ofInterest radius Rs equal to the optimum radius values Ro during theconsidered observation time interval [Tsn, Ten] in any days.

In one embodiment of the present invention, the step g) of estimating anumber An of persons that gathered in the Area of Interest having theArea of Interest radius Rs equal to the optimum radius value Ro furthercomprises: combining the third number Un|_(AOI) of User Equipment andthe further average User Equipment number μn|_(AOI) in order to obtainthe number An of persons that gathered in the Area of Interest havingthe Area of Interest radius equal to the optimum radius value.

In one embodiment of the present invention, combining the third numberUn|_(AOI) of User Equipment and the further average User Equipmentnumber μn|_(AOI) comprises subtract the further average User Equipmentnumber μn|_(AOI) from the third User Equipment number Un|_(AOI).

In one embodiment of the present invention, the gathering of persons atan Area of Interest during an observation time interval on a daycomprises a plurality of gathering of persons at the Area of Interestduring the observation time interval [Tsn, Ten] on respective days gn,the method further comprising the step of: h) iterating steps b) to e)for each gathering of persons, and wherein the step f) of computing anoptimum radius value Ro of the Area of Interest radius Rs as the averageof the computed radius values Rk within which the gathering of personsis detected, comprises: computing an optimum radius value Ro of the Areaof Interest radius Rs as the average of the computed radius values Rkweighted by a number of detected gathering of persons DSk within theArea of Interest having the Area of Interest radius Rs equal to the samecomputed radius values Rk, said number of detected gathering of personsDSk being the sum of the gatherings of persons determined by iteratingstep e).

In one embodiment of the present invention, the step g) of estimating anumber An of persons that gathered in the Area of Interest having theArea of Interest radius equal to the optimum radius value is iteratedfor each gathering of persons of the plurality of gathering of persons.

Another aspect of the present invention proposes a system coupled with awireless telecommunication network for estimating a number of persons Anthat gathered at an Area of Interest, the system comprising: acomputation engine adapted to process data retrieved from a mobiletelephony network; a repository adapted to store data regardinginteractions between the User Equipment and the mobile telephonynetwork, computation results generated by the computation engine and,possibly, any processing data generated by and/or provided to thesystem, and an administrator interface operable for modifying parametersand/or algorithms used by the computation engine and/or accessing datastored in the repository. Moreover the system further comprises a memoryelement storing a software program product configured for implementingthe method of above through the system.

In one embodiment of the present invention, the system further comprisesat least one user interface adapted to receive inputs from, and toprovide output to a user of the system, the user comprising one or morehuman beings and/or one or more external computing systems subscriber ofthe services provided by the system.

One of the advantages of the solution according to the present inventionis that it is computationally simple, involving just operations ofcounting and algebraic operations.

BRIEF DESCRIPTION OF THE DRAWINGS

These and others features and advantages of the solution according tothe present invention will be better understood by reading the followingdetailed description of an embodiment thereof, provided merely by way ofnon-limitative examples, to be read in conjunction with the attacheddrawings, wherein:

FIG. 1 is a schematic representation of a crowd estimation systemaccording to an embodiment of the present invention;

FIGS. 2A-2E are exemplary shapes that surface elements may takeaccording to an embodiment of the present invention;

FIGS. 3A-3B are examples of covered geographic regions associated with amobile communication network subdivided in corresponding sets of surfaceelements according to an embodiment of the present invention;

FIGS. 4A-4E are exemplary shapes that the AoI to be determined may takeaccording to an embodiment of the present invention;

FIGS. 5A-5D are relevant surface elements among the surface elements inwhich the covered geographic region is subdivided according to anembodiment of the invention, and

FIGS. 6A-6C are a schematic flowchart of a public happenings evaluationalgorithm according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

With reference to the drawings, FIG. 1 is a schematic representation ofa crowd estimation system, simply denoted as system 100 hereinafter,according to an exemplary embodiment of the present invention.

The crowd estimation system and method described in the following allowperforming an estimation of a number of persons in a crowd gathered forexample, in order to attend at one or more public happenings, of themost disparate nature, like for example (and non-exhaustively) livetelevision shows, artistic/entertaining performances, culturalexhibitions, theatrical plays, sports contests, concerts, movies,demonstrations and so forth.

In addition, as will be clearly understood the crowd estimation systemand method described in the following also allow performing anestimation of a number of persons in a crowd gathered for visiting aplace of particular interest such as for example a museum, a monument, ahistorical building and so forth.

The system 100 is coupled with a mobile communication network 105, suchas a (2G, 3G, 4G or higher generation) mobile telephony network.

The mobile communication network 105 is able to provide communicationresources (e.g., a portion of an available communication bandwidth) toUser Equipment, UE in the following (e.g. a mobile phone, a smartphone,a tablet with 2G-3G-4G connectivity, etc.) requesting them in a coveredgeographic region (not detailed in FIG. 1, but described in thefollowing with reference to FIGS. 3A and 3B). In other words, UE withinthe covered geographic region may be served by the mobile communicationnetwork 105.

In one embodiment of the present invention, the covered geographicregion may comprise a whole territory covered (i.e., served) by themobile communication network 105, even though, in other embodiments ofthe present invention, a covered geographic region comprising only aportion of the whole territory covered by the mobile communicationnetwork 105 could be considered.

The mobile communication network 105 comprises a plurality (i.e., two ormore) of communication stations 105 a (e.g., radio base stations of themobile telephony network) deployed within the covered geographic region.

Each communication station 105 a is adapted to manage communications ofUE (not shown, such as for example mobile phones) in one or more servedareas or cells 105 b (in the example at issue, three cells are served byeach communication station 105 a).

Accordingly, the covered geographic region comprises the area of aplurality of the cells 105 b of the mobile communication network 105,for example in one embodiment of the invention the sum of the areas ofall the cells 105 b of the mobile communication network 105 builds upthe covered geographic region.

Differently, an (geographic) Area of Interest, AoI in brief 107schematized in FIG. 1 as the area within the dash-and-dot line 107extends over one or more cells 105 b of the mobile communication network105. The AoI 107 is an area within which the people gathered in a crowdfor example, in order to attend at one or more public happenings andwhose extent is determined by the crowd estimation algorithm of thepresent invention (as described in the following).

The system 100 is configured for receiving from the mobile communicationnetwork 105 aggregated data regarding the usage of the mobilecommunication network 105 within one or more reference time intervals(as described in the following).

The term ‘aggregated data’, as used in the present disclosure, indicatesdata regarding the operation of the mobile communication network 105,such as for example a number of served UE traffic load, number of voicecalls, number of SMS transmitted, volume of binary data exchanged, etc.The aggregated data are typically used by an operator managing themobile communication network 105 for analysing general trends or values(e.g., changes of a number of served UE over time) in the exploiting ofresources (e.g., bandwidth and/or computational capabilities) of themobile communication network 105 without identifying each single UE (andtherefore the owners of the UE) that interacted with the mobilecommunication network 105.

Particularly, the aggregated data do not comprise any identifier of theUE served by the mobile communication network 105, therefore noindication about the UE owners identities, habits or frequented places(such as for example home and work places) may be obtained from theaggregated data provided by the mobile communication network 105, thusthe privacy of the UE owners is ensured.

In one embodiment of the invention, the aggregated data acquired by thesystem 100 from the mobile communication network 105 comprise anindication regarding a number of UE, i.e. indicative also of a number ofindividuals (the UE owners), located in the covered geographic region.

The covered geographic region just described comprises the AoI 107.

The AoI 107 (further described in the following) may generally comprisea core place (e.g., a stadium, a theater, a city square and so on) whereone or more public happenings, which attracted respective crowds, havetaken place and, possibly, surroundings (e.g., nearby parking lots,nearby streets, nearby transport stations and so forth) of the coreplace.

It should be noted that since the AoI 107 is comprised in the coveredgeographic region of the mobile communication network 105, thus UEwithin the AoI 107 may be served by the mobile communication network105.

Preferably, the aggregated data received from the mobile communicationnetwork 105 by the system 100 comprise an indication of a number of UEserved by the mobile communication network 105.

More preferably, the aggregated data received from the mobilecommunication network 105 by the system 100 comprise indications of theUE number served by the communication network 105 in a plurality ofsub-portions, or surface elements (described in the following), of thecovered geographic region. For example, each surface element maycomprise one of the cells 105 b, a group of two or more cells 105 b,and/or portions of the cells 105 b of the mobile communication network105 (e.g., in one embodiment of the invention the surface elements maybe shaped as squares having a side of 150 m, therefore each one of thecells 105 b comprises more than one surface element especially inextra-urban areas where cells 105 b usually have a greater extent withrespect to cells 105 b in urban areas).

Generally, each communication station 105 a of the mobile communicationnetwork 105 is adapted to interact with any UE located within one of thecells 105 b served by such communication station 105 a (e.g.,interactions at power on/off, at location area update, atincoming/outgoing calls, at sending/receiving SMS and/or MMS, atInternet access etc.). Such interactions between UE and mobilecommunication network 105 will be generally denoted as events e_(i)(i=1, . . . , I; where I is an integer) in the following.

Therefore, aggregated data comprising the indication of a number of UEmay be computed by simply counting a number of UE that had at least oneinteraction with the mobile communication network 105. In other words,the aggregated data comprise an indication of the number of UE thatproduced at least an event e_(i) with one of the communication stations105 a that provides services over respective cells 105 b of the mobilecommunication network 105.

Alternatively, the indication of a number of UE may be based on trafficload experienced by the mobile communication network 105. Indeed, eachevent e_(i) in order to be performed, requires a portion ofcommunication resources (e.g., portions of a communication bandwidth)managed by the mobile communication network 105, i.e. each event e_(i)produces a certain amount of traffic load. Accordingly, the aggregateddata preferably provide an estimation of a number of UE on the basis ofa traffic load divided by an average UE traffic load (i.e., an averagetraffic load generated by each UE associated with the mobilecommunication network 105).

In the present disclosure it is assumed that aggregated data areprovided by the mobile communication network 105 periodically, e.g. atthe lapse of predetermined reference time intervals (e.g., every certainnumber of minutes, hours, on a daily or weekly basis) according to acapability of the mobile communication network 105 of collecting,processing and providing such aggregated data.

For example, in one embodiment of the present invention, the aggregateddata are collected by the mobile communication network 105 with aperiodicity equal to, or lower than, fifteen (15) minutes (i.e., thereference time intervals have a duration of fifteen minutes each), whichis a periodicity sustainable by computational capabilities of presentmobile communication networks.

Nevertheless, the mobile communication network 105 may be configured toprovide aggregated data asynchronously or, alternatively, aggregateddata may be provided by a third processing module associated with themobile communication network 105 for receiving the data regardingoperation of the mobile communication network 105 and with the system100 for providing the aggregated data without departing from the scopeof the present invention. The system 100 comprises a computation engine110 configured to process aggregated data retrieved from the mobilecommunication network 105, and a repository 115 (such as a database, afile system, etc.) configured to store the aggregated data received fromthe mobile communication network 105, computation results generated bythe computation engine 110 and, possibly, any processing data generatedby, and/or provided to, the system 100 (generally in a binary format).The system 100 is provided with an administrator interface 120 (e.g., acomputer) configured and operable to modify parameters and/or algorithmsused by the computation engine 110 and/or to access data stored in therepository 115.

Preferably, the system 100 comprises one or more user interfaces 125(e.g., a user terminal, a software running on a remote terminalconnected to the system 100) adapted to receive inputs from, and toprovide outputs to a user of the system 100. The term “user of thesystem” as used in the present disclosure may refer to one or more humanbeings and/or to external computing systems (such as a computer network,not shown) of a third party being subscriber of the services provided bythe system 100 and enabled to access the system 100—e.g., undersubscription of a contract with a service provider owner of the system100, and typically with reduced right of access to the system 100compared to the right of access held by an administrator of the system100 operating through the administrator interface 120.

It should be appreciated that the system 100 may be implemented in anyknown manner; for example, the system 100 may comprise a singlecomputer, or a network of distributed computers, either of physical type(e.g., with one or more main machines implementing the computationengine 110 and the repository 115, connected to other machinesimplementing administrator and user interfaces 120 and 125) or ofvirtual type (e.g., by implementing one or more virtual machines in acomputer network).

Preferably, the computation engine 110 processes a crowd estimationalgorithm (described in the following) implemented by a software programproduct stored in a memory element 110 a of the system 110, comprised inthe computation engine 110 in the example of FIG. 1, even though thesoftware program product could be stored in the repository 115 as well(or in any other memory element provided in the system 100).

In operation, the aggregated data may be continuously retrieved by thesystem 100 from the mobile communication network 105. For example,aggregated data may be transferred from the mobile communication network105 to the system 100 as they are generated, in a sort of “push”modality.

Alternatively, aggregated data may be collected by the mobilecommunication network 105 with a first periodicity (e.g., every 15minutes) and then packed and transferred to the system 100 with a secondperiodicity lower than the first periodicity (e.g., every 24 hours), oronly upon request by the system 100.

The aggregated data retrieved from the mobile communication network 105are preferably stored in the repository 115, where they are madeavailable to the computation engine 110 for processing.

The aggregated data are processed according to instructions provided bythe system administrator (through the administrator interface 120), forexample stored in the repository 115 and, possibly, according toinstructions provided by a user (through the user interface 125).Finally, the computation engine 110 provides the results of theprocessing performed on the aggregated data to the user through the userinterface 125, and optionally stores such processing results in therepository 115.

It should be further noted that the method described in the presentdisclosure may be implemented by using any source of data (e.g.,provided by one or more among WiFi, WiMax, Bluetooth networks orcombinations thereof with mobile telephony networks) from which it ispossible to obtain aggregated data that could be related to a number ofperson within a predetermined area (e.g., the surface elements, or theAoI 107).

Turning now to FIGS. 2A-2E, they are exemplary shapes in which surfaceelements 205 of the covered geographic region associated with the mobilecommunication network 105 (i.e., the covered geographic region in whichthe mobile communication network 105 is able to serve UE) may be modeledaccording to an embodiment of the present invention.

For the purposes of the present invention, each surface element 205 ofthe geographic region covered by the mobile communication network 105may be modeled as a surface (as shown in FIG. 2A) having a respectivesurface center B and a respective surface radius Rs.

According to an embodiment of the present invention, generally thesurface center B and the surface radius Rs of the surface element 205are not related with a geographic position of the one or morecommunication stations 105 a or the positions of the cells 105 b of themobile communication network 105.

As previously noted, the surface elements 205 may extend over one ormore cells 105 b of the mobile communication network 105, or conversely,surface elements 205 may be smaller than a cell 105 b of the mobilecommunication network 105.

It should be noted that surface elements 205 are not limited to adisc-like shape, in facts, the surface elements 205 may have the shapeof a, preferably although not strictly necessarily regular, polygon. Inthis case, the surface center B corresponds to a center of mass (orcentroid) of the polygon, while the surface radius Rs corresponds to asegment adjoining the center of mass of the polygon, i.e. the surfacecenter B, with a vertex of the polygon (as shown in FIGS. 2B and 2D) orwith a midpoint of a side of the polygon (as shown in FIGS. 2C and 2E).Alternatively, the mobile communication network 105 may be modeled bymeans of a Voronoi tessellation diagram, in which each Voronoi cellcorresponds to a surface element 205 of the geographic region covered bythe mobile communication network 105 (since Voronoi tessellationdiagrams are well known in the art, they are not discussed furtherherein).

The modeling, the list and the number of the surface elements 205 of thecovered geographic region associated with the mobile communicationnetwork 105 may be predetermined by the mobile communication network105, by the system 100, or are inputted to the system 100 by theadministrator through the administrator interface 120.

Considering FIGS. 3A and 3B, they are examples of covered geographicregions 300 and 300′ associated with the mobile communication network105 subdivided in corresponding sets of surface elements 205 ₁₋₉ and205′₁₋₉, respectively, according to an embodiment of the presentinvention.

The covered geographic region 300 shown in FIG. 3A has been subdividedin nine surface elements 205 ₁₋₉ having the shape of a regular polygon,i.e. a square. Conversely, the geographic region 300′ shown in FIG. 3Bhas been subdivided in nine surface elements 205′₁₋₉ having the shape ofirregular polygons.

Generally, the geometric features of the surface elements 205 ₁₋₉ and205′₁₋₉ may be based upon a number of parameters of the geographicregion, such as for example urban features (presence and distribution ofstreets, wards, etc.) and/or natural features (presence anddistributions of rivers, hills, etc.). Moreover, other references and/ormapping systems (such as for example well-known network planningsoftware tools used by provider of the mobile communication network 105)may be considered for defining the shapes and sizes of the surfaceelements 205 ₁₋₉ and 205′₁₋₉ in addition or as an alternative to urbanand natural features.

It should be noted that nothing prevents to define shapes and sizes ofthe surface elements 205 ₁₋₉ and 205′₁₋₉ according to a distribution ofthe cells 105 b of the mobile communication network 105.

Furthermore, generally there are no relationships among number, shapesand sizes of the surfaces elements 205 ₁₋₉ and 205′₁₋₉ and the AoI 107.

In one embodiment of the present invention, square surface elements arepreferably used such as the surface elements 205 ₁₋₉ of the coveredgeographic region 300. Even more preferably, the surface elements 205₁₋₉ correspond to pixels determined during the network planning by thenetwork planning software tools mentioned above.

Indeed, square surface elements 205 ₁₋₉ allow simply subdividing thecovered geographic region 300, e.g. having determined a reference pointof a generic surface element 205 _(q) (such as for example the surfacecenter B) and the size of the sides of the square surface elements 205₁₋₉, it is simply possible to determine the vertexes and the surfacecenters B of all of the surface elements 205 ₁₋₉.

For example, the pixels used as surface elements may be shaped assquares having a side having a size comprised between 200 m and 50 msuch as 150 m. This ensures a good trade-off between the detail level ofthe covered geographic region 300 and the computational complexityrequired to analyze the aggregated data referred to the coveredgeographic region 300.

According to one embodiment of the present invention, the aggregateddata provided by the mobile communication network 105 comprise anindication regarding a number of UE (and therefore of UE owners) foreach one of the of the surfaces elements 205 ₁₋₉ and 205′₁₋₉.

In the following, reference is made only to the coverage geographic area300 and to the respective surface elements 205 ₁₋₉ of FIG. 3A for thesake of simplicity and brevity; however, it should be noted that similarconsiderations may be applied to the coverage geographic area 300′ andto the respective surface elements 205′₁₋₉ of FIG. 3B as well.

In one embodiment of the invention, for each generic surface element 205_(q) (q=1, . . . , Q; where Q is a positive integer, Q=9 in the exampleof FIG. 3A) the system 100 receives from the mobile communicationnetwork 105 a respective aggregated UE number u_(q).

Preferably, the system 100 receives from the mobile communicationnetwork 105 a plurality of aggregated UE numbers u_(q,t) for eachgeneric surface element 205 _(q). Each UE number u_(q,t) of theaggregated UE numbers u_(q,t) is referred to a t-th reference timeinterval of a plurality of consecutive reference time intervals dr (t=1,. . . , T; where T is a positive integer).

In other words, the system 100 receives a set {u_(q,t)} of Q aggregatedUE numbers u_(q,t) (one for each one of the surface elements 205 _(q);i.e., nine aggregated UE numbers u_(q,t) in the example of FIG. 3A),each set being referred to a reference time interval dr of consecutive Treference time intervals dr, e.g. each set {u_(q,t)} of Q aggregated UEnumbers u_(q,t) is generated at time instants t_(r) corresponding to theend of a respective time interval dr.

For example, by considering an acquisition period ΔT twenty four hourslong (ΔT=24 hr), during which T sets {u_(q,t)} of Q aggregated UEnumbers u_(q,t) are received by the system 100 for storing and/orprocessing (as described in the following), and time interval dr fifteenminutes long (dt=15 min.), at the end of the acquisition period ΔT, thesystem 100 has available T=96 sets {u_(q,t)} of Q=9 aggregated UEnumbers u_(q,t), one for each reference time interval dr. Indeed, theacquisition period ΔT is subdivided in 96 consecutive reference timeintervals dr that have the following structure: d₁=[00:00, 00:15),d₂=[00:15, 00:30), . . . , d₉₅=[23:30, 23:45), and d₉₆=[23:45, 00:00).

According to an embodiment of the present invention, the aggregated UEnumbers u_(q,t) are computed on the basis of traffic loads of each cell105 b of the mobile communication network 105 during the correspondingreference time interval dr.

Preferably, the aggregated UE number u_(q,t) is computed by combiningthe traffic load (e.g., in Erlang) measured at each one of the cells 105b comprised in the coverage geographic area 300 during the correspondingt-th reference time interval dr with the average UE traffic loadestimated for UE in the cells 105 b (i.e., an average traffic loadgenerated by each UE associated with the mobile communication network105).

The traffic load of each cell 105 b is divided by the average UE trafficload, thus obtaining estimation of the number of UE served by each thecell 105 b during the reference time interval dr. Subsequently, thenumber of UE served by cells 105 b are distributed among the surfaceelements 205 _(q) of the covered geographic region 300.

For example, the determination (i.e., the distribution) of the number ofUE within each one of the surface elements 205 _(q) of the coveredgeographic region 300 may be based on the method described in the paperby Francesco Calabrese, Carlo Ratti: “Real Time Rome”, Networks andCommunications Studies 20(3-4), pages 247-258, 2006 mentioned above onthe basis of the number of UE served by cells 105 b.

It is pointed out that the present invention is independent from thequantity used for determining the aggregated UE number u_(q,t). Indeed,aside the traffic load measured in Erlangs, other forms accounting thetraffic load may be used, e.g. a number of calls per cell 105 b, anumber of connections per cell 105 b or the number of unique UEconnected per cell 105 b (i.e., each referred to the corresponding t-threference time interval d_(t)).

It is also pointed out that the present invention is independent from afunction used to distribute the numbers of UE served by cells 105 bamong the surface element 205 _(q) of the covered geographic region 300.

In another embodiment of the present invention, the mobile communicationnetwork 105 has the knowledge of the number of UE connected to each oneof the cells 105 b, therefore, there is no need to determine the numberof UE through the traffic load, and the aggregated UE numbers u_(q,t)may be straightforwardly determined by adding together the number of UEserved by the cells 105 b comprised in the surface element 205 _(q)during the respective reference time interval dr.

Turning now to FIGS. 4A-4E, they are exemplary shapes that the AoI 107to be determined may take according to an embodiment of the presentinvention.

Generally, the AoI 107 for one or more public happenings may be modeledas an area having an AoI center C and an AoI radius Ra. For example, theAoI 107 may be delimited by a circumference centered in the AoI center Cand having the AoI radius Ra as circumference radius (as shown in FIG.4A).

It should be noted that the AoI 107 may have shapes different from thecircumference. For example, the AoI 107 may have the shape of a,preferably regular, polygon. In this case, the AoI center C correspondsto a center of mass (or centroid) of the polygon, while the AoI radiusRa corresponds to a segment adjoining the center of mass of the polygonwith a vertex of the polygon (as shown in FIGS. 4B and 4D) or with amidpoint of a side of the polygon (as shown in FIGS. 4C and 4E) in asimilar way as for the surface elements 205 b modeling discussed above.

The AoI center C may be set (e.g., by a user through the user interface125 or by a system administrator through the administrator interface120) as a (geographical) central point of the AoI 107 (e.g., ageographical central point of the core place), as an address of the coreplace of the one or more public happenings, as a point provided by amapping software, such as web mapping services (e.g., Google Maps™,OpenStreetMap™, etc.).

As will be described in more detail in the following, the AoI radius Ramay take zero or negative values along with positive values. In case theAoI radius Ra takes zero or negative values, the AoI 107 is limited tothe AoI center C (i.e., the core place of the one or more publichappenings). The meaning of zero or negative values for the AoI radiusRa will be further clarified by reference to such zero or negativevalues in the embodiments described below.

The algorithm described in the following is configured to determine anoptimum radius value Ro for the AoI radius Ra of the AoI 107. In oneembodiment of the invention, the optimum radius value Ro is determinedby means of iterative steps starting from a minimum radius value Rmin toa maximum radius value Rmax (as described hereinbelow). Preferably, theminimum radius value Rmin and the maximum radius value Rmax are set bythe administrator of the system 100 through the administrator interface120.

In an embodiment of the present invention, on the basis of statisticalanalysis of empirical data regarding a plurality of past publichappenings the minimum radius value Rmin is set equal to −1500 m(Rmin=−1500 m), while the maximum radius value Rmax is set equal to 1500m (Rmax=1500 m).

Having defined the shape of the surface elements 205 b and the shape ofthe AoI 107, the concept of relevant surface element, i.e., a surfaceelement 205 _(q) of the covered geographic region 300 that is consideredat least partially belonging to the AoI 107 according to an embodimentof the invention will be now be introduced.

FIGS. 5A-5D are relevant surface elements 505 a-d among the surfaceelement 205 _(q) of the covered geographic region with respect to theAoI 107 according to an embodiment of the invention.

In one embodiment of the invention, given the AoI 107 having the AoIcenter C and the surface element 205 _(q) having the surface center Band the surface radius Rs, the generic surface element 205 q may beconsidered a relevant surface element 505 a-d for the AoI 107 if thefollowing inequality is verified:

Dist(C,B)≤|Rs+Ra|,  (1)

where Dist(C, B) is the geographical distance between the AoI center Cand the surface center B.

According to the value of the AoI radius Ra of the AoI 107, inequality(1) may take three different meanings.

Namely, if the AoI radius Ra of the AoI 107 is greater than zero (i.e.,Ra>0), inequality (1) reduces to:

Dist(C,B)≤(Rs+Ra)  (2)

and the generic surface element 205 b is considered a relevant surfaceelement (such as the case of relevant surface element 505 a in FIG. 5A)for the AoI 107 having an AoI radius Ra greater than zero if the area ofthe AoI 107 and the generic surface element 205 q are at least partiallysuperimposed (even if the AoI center C fall outside the generic surfaceelement 205 q).

If the AoI radius Ra of the AoI 107 is equal to zero (i.e., Ra=0) theinequality (1) reduces to:

Dist(C,B)≤Rs  (3)

and the generic surface element 205 q is considered a relevant surfaceelement (such as the case of relevant surface elements 505 b and 505 cin FIGS. 5B and 5C) for the AoI 107 having an AoI radius Ra equal tozero if the AoI center C of the AoI 107 is comprised in the genericsurface element 205 q.

Finally, if the AoI radius Ra of the AoI 107 is smaller than zero (i.e.,Ra<0) the generic surface element 205 q is considered a relevant surfaceelement (such as the case of relevant surface element 505 d in FIG. 5D)for the AoI 107 having an AoI radius Ra smaller than zero if the AoIcenter C of the AoI 107 is comprised within the generic surface element205 q at a distance from the surface center B equal to or smaller thanRs−|Ra|.

A generic public happening S, apart from being held at a specificlocation (i.e., the AoI 107), has a start time Ts and an end time Te.Consequently, for the purposes of the present invention the genericpublic happening S has a relevant duration equal to an observation timeinterval [Ts, Te] (i.e., a time interval that starts at the a start timeTs and ends at the end time Te, lasting for Te−Ts time units, e.g.seconds, minutes or hours).

Both the start time Ts and the end time Te may be defined so as tocorrespond to the official (officially announced) start and end timesscheduled for that generic public happening S. Nevertheless, theApplicant has observed that by anticipating the start time Ts withrespect to the official start time of the generic public happening S itis possible to take into account the fact that people (i.e., UE ownersthat attend at the generic public happening S) arrive at the AoI 107before the official start time of the generic public happening S, whichmay be useful for collecting data about a trend in time of a flow ofattendees arriving at the generic public happening S. For example, onthe basis of empirical data of previous public happenings, the Applicanthas found that the start time Ts may be usefully anticipated to 60minutes before the official start time of the generic public happening Sin order to take into account the trend of attendees arriving at thegeneric public happening S.

Similarly, the Applicant has observed that the end time Te may bedelayed with respect to the official end time of the generic publichappening S in order to take into account the fact that people leave theAoI 107 after the official end time of the generic public happening S,which may be useful for collecting data about a trend in time of a flowof attendees leaving the generic public happening S. For example, on thebasis of empirical data of previous public happenings, the Applicant hasfound that the end time Ts may be usefully delayed by 30 minutes afterthe official end time of the generic public happening S in order to takeinto accounts the trend of attendees leaving the generic publichappening S.

Anyway, the administrator through the administrator interface 120,and/or the user through the user interface 125, may set any custom starttime Ts and end time Te for the generic public happening S. For example,the start time Ts and the end time Te may be set in order to define theobservation time interval [Ts, Te] shorter than the effective durationof the generic public happening S (i.e., shorter than the duration ofthe whole public happening) in order to analyze a number or a variationof persons in the crowd attending at the generic public happening S onlyduring a sub-portion of the whole time duration of the generic publichappening S.

Having described the system 100, and the time (i.e., the start time Tsand the end time Te) and spatial (i.e., the AoI center C and AoI radiusRa of the AoI 107) characteristics of a generic public happening S, acrowd estimation algorithm (or crowd counting algorithm) of personsattending at one or more public happenings according to an embodiment ofthe present invention will be now described, by making reference toFIGS. 6A-6C, which are a schematic block diagram thereof.

Let N (where N is an integer number, that may be defined by theadministrator through the administrator interface 120 and/or by the userthrough the user interface 125) be a number of public happenings Sn,where n is a happening variable indicating which of the N publichappenings is considered (i.e., 1≤n≤N), held in a same AoI 107 of whichthe number of persons in the respective crowd attending thereat is to bedetermined.

For each public happening Sn, an observation day gn during which thepublic happening Sn has been held, the start time Tsn and the end timeTen are defined. It should be noted that the start time Tsn and the endtime Ten may vary from one public happening Sn to the other.

Moreover, for each public happening Sn a set of previous days gpn (where1≤p≤P and P is an integer number) preceding the observation day gn areconsidered. The number P of previous days gpn considered is preferablyset by the administrator (through the administrator interface 120). Inan embodiment of the present invention, the administrator sets thenumber P of previous days gpn according to the storage capabilities ofthe repository 115 (i.e., in order to be able to store all the dataregarding the P previous days gpn) and/or on the basis of computationalcapabilities of the computation engine 110 (i.e., in order to be able toprocess all the data regarding the P previous days gpn). Preferably, theadministrator sets the number P of previous days gpn also on the basisof a statistical analysis of past public happenings of the same kind(i.e., cultural, entertaining, politics or sport shows).

The Applicant has found that by setting the number P of previous daysgpn equal to 6 (i.e., P=6) provides good results for most kind of publichappenings (although this should not be construed as limitative for thepresent invention).

A first portion of the crowd estimation algorithm is configured todetermine the optimum radius value Ro for the AoI radius Ra of the AoI107 on the basis of the data regarding all the N public happening Snconsidered.

Initially (step 602) the AoI center C, the observation days gn and thestart times Tsn and end times Ten are inputted to the system 100, e.g.by a user through the user interface 125 or by the administrator throughthe administrator interface 120.

Afterwards (step 604), an iteration variable k is initialized to zero(i.e., k=0), a detected number of happening variable DSk is initializedto zero as well (i.e., DSk=0) and a calculated radius value Rk isinitially set to the minimum radius value Rmin (i.e., Rk=Rmin). Theiteration variable k accounts for the number of iterations of the firstportion of the algorithm, the detected number of happening variable DSkaccounts for the number of public happenings Sn detected during theiterations of the first portions of the algorithm (as described in thefollowing) and the calculated radius value Rk is used in determining theoptimum radius value Ro.

Next (step 606), the relevant surface elements 505 a-d for the AoI 107having a AoI radius Ra equal to the calculated radius value Rk (Ra=Rk)are identified by means of the inequality (1) as described above.

Afterwards (step 608), the day variable n is initialized, e.g. to unity(n=1).

All the sets {u_(q,t)} of Q aggregated UE numbers u_(q,t) referred tothe observation day gn during an observation time interval [Tsn, Ten](i.e., referred to time intervals dr comprised in the observation timeinterval [Tsn, Ten]) and referred to the relevant surface elements 505a-d determined at step 606 are retrieved (step 610) from the repository115.

Subsequently (step 612), a first UE number Unk is computed as the numberof UE in the relevant surface elements 505 a-d on the basis of the sets{u_(q,t)} of Q aggregated UE numbers u_(q,t) that have been retrieved atprevious step 606 (the first UE number Unk depends on the relevantsurface elements and, therefore, on the calculated radius value Rk).

The first UE number Unk may be computed as a total number, an averagenumber, or a maximum (peak) number of UE in the relevant surfaceelements 505 a-d, for example according to a setting selected by theadministrator of the system 100 through the administrator interface 120and/or by the user of the system 100 through the user interface 125.

For example, the first UE number Unk as the total UE number in therelevant surface elements 505 a-d may be computed in the followingmanner. Firstly, the sum of the values of aggregated UE numbers u_(q,t)(determined at step 610) within each reference time intervals drcomprised in the observation time interval [Tsn, Ten] during theconsidered day gn in all the relevant surface elements 505 a-d(determined at step 606) is computed. Subsequently, a sum of the valuesjust computed for each reference time intervals dr comprised in theobservation time interval [Tsn, Ten] during the considered day gn isperformed. In other words, the first UE number Unk as the total UEnumber in the relevant surface elements 505 a-d may be computed as:

$\begin{matrix}{{Unk} = {\sum\limits_{t \in {\lbrack{{Tsn},{Ten}}\rbrack}}^{\;}{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}^{\;}u_{q,t}} ).}}} & ( {4a} )\end{matrix}$

Similarly, the first UE number Unk as the average UE number in therelevant surface elements 505 a-d may be computed in the followingmanner. Firstly, the sum of the values of aggregated UE numbers u_(q,t)(determined at step 610) within each reference time intervals drcomprised in the observation time interval [Tsn, Ten] during theconsidered day gn in all the relevant surface elements 505 a-d(determined at step 606) is computed. Subsequently, a sum of the valuesjust computed for each reference time intervals dr comprised in theobservation time interval [Tsn, Ten] during the considered day gn isperformed. Finally, the just obtained value is divided by the number T′of reference time intervals dr comprised in the observation timeinterval [Tsn, Ten] during the considered day gn. In other words, thefirst UE number Unk as the average UE number in the relevant surfaceelements 505 a-d may be computed as:

$\begin{matrix}{{Unk} = {\frac{1}{T^{\prime}}{\sum\limits_{t \in {\lbrack{{Tsn},{Ten}}\rbrack}}^{\;}{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}^{\;}u_{q,t}} ).}}}} & ( {4b} )\end{matrix}$

Conversely, the first UE number Unk as the maximum (peak) UE number inthe relevant surface elements 505 a-d may be computed in the followingmanner. Firstly, the sum of the values of aggregated UE numbers u_(q,t)(determined at step 610) within each reference time intervals drcomprised in the observation time interval [Tsn, Ten] during theconsidered day gn in all the relevant surface elements 505 a-d(determined at step 606) is computed. Subsequently, the maximum valueamong the values just computed is selected as the first UE number Unk.In other words, the first UE number Unk as the maximum (peak) UE in therelevant surface elements 505 a-d may be computed as:

$\begin{matrix}{{Unk} = {\underset{t \in {\lbrack{{Tsn},{Ten}}\rbrack}}{\overset{\;}{Max}}{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}^{\;}u_{q,t}} ).}}} & ( {4c} )\end{matrix}$

It should be noted that the first UE number Unk, regardless whether itis computed as total number, an average number, or a maximum (peak)number of UE in the relevant surface elements 505 a-d, is alwaysdependent on the calculated radius value Rk used to determine therelevant surface elements 505 a-d to which the aggregated UE numbersu_(q,t) are referred.

Similarly, all the sets {u′_(q,t)} of Q aggregated UE numbers u′_(q,t)referred to the previous days gpn during the observation time interval[Tsn, Ten] and having taken place within the relevant surface elements505 a-d determined at step 606 are retrieved (step 614) from therepository 115.

Then (step 616), it is computed a second UE number Upnk for each one ofthe previous days gpn as the number of UE in the relevant surfaceelements 505 a-d on the basis of the sets {U′_(q,t)} of Q aggregated UEnumbers u′_(q,t) referred to relevant surface elements 505 a-d that havebeen retrieved at previous step 606 (the second UE numbers Upnk dependson the relevant surface elements 505 a-d and, therefore, on thecalculated radius value Rk).

Similarly to the first UE number Unk, each one of the second UE numbersUpnk may be computed as a total number, an average number, or a maximum(peak) number of UE in the relevant surface elements 505 a-d, forexample according to a setting selected by the administrator of thesystem 100 through the administrator interface 120 and/or by the user ofthe system 100 through the user interface 125.

For example, the second UE numbers Upnk as the total numbers of UE inthe relevant surface elements 505 a-d may be computed in the followingmanner. Firstly, for each one of the previous days gp, the sum of thevalues of aggregated UE numbers u′_(q,t) (determined at step 614) withineach reference time intervals dr comprised in the observation timeinterval [Tsn, Ten] during the considered previous day gp (preceding theconsidered day gn) in all the relevant surface elements 505 a-d(determined at step 606) is computed. Subsequently, a sum of the valuesjust computed for each reference time intervals dr comprised in theobservation time interval [Tsn, Ten] during the considered the previousday gp (preceding the considered day gn) is performed. In other words,the second UE number Upnk for the previous day gp (preceding theconsidered day gn) as the total UE number in the relevant surfaceelements 505 a-d may be computed as:

$\begin{matrix}{{Upnk} = {\sum\limits_{t \in {\lbrack{{Tsn},{Ten}}\rbrack}}^{\;}{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}^{\;}u_{q,t}^{\prime}} ).}}} & ( {5a} )\end{matrix}$

The second UE numbers Upnk as the average UE numbers in the relevantsurface elements 505 a-d may be computed in the following manner.Firstly, for each one of the previous days gp, the sum of the values ofaggregated UE numbers u′_(q,t) (determined at step 614) within eachreference time intervals dr comprised in the observation time interval[Tsn, Ten] during the considered previous day gp (preceding theconsidered day gn) in all the relevant surface elements 505 a-d(determined at step 606) is computed. Subsequently, a sum of the valuesjust computed for each reference time intervals dr comprised in theobservation time interval [Tsn, Ten] during the considered the previousday gp (preceding the considered day gn) is performed. Finally, the justobtained value is divided by the number T′ of reference time intervalsdr comprised in the observation time interval [Tsn, Ten] during theconsidered the previous day gp (preceding the considered day gn). Inother words, the second UE numbers Upnk for the previous day gp(preceding the considered day gn) as the average UE numbers in therelevant surface elements 505 a-d may be computed as:

$\begin{matrix}{{Upnk} = {\frac{1}{T^{\prime}}{\sum\limits_{t \in {\lbrack{{Tsn},{Ten}}\rbrack}}^{\;}{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}^{\;}u_{q,t}^{\prime}} ).}}}} & ( {5b} )\end{matrix}$

The second UE numbers Upnk as the maximum (peak) UE numbers in therelevant surface elements 505 a-d may be computed in the followingmanner. Firstly, for each one of the previous days gp, the sum of thevalues of aggregated UE numbers u′_(q,t) (determined at step 614) withineach reference time intervals dr comprised in the observation timeinterval [Tsn, Ten] during the considered previous day gp (preceding theconsidered day gn) in all the relevant surface elements 505 a-d(determined at step 606) is computed. Subsequently, the maximum valueamong the values just computed is selected as the second UE number Upnkfor the considered previous day gp (preceding the considered day gn). Inother words, the second UE number Upnk for the considered previous daygp as the maximum (peak) UE in the relevant surface elements 505 a-d maybe computed as:

$\begin{matrix}{{Upnk} = {\underset{t \in {\lbrack{{Tsn},{Ten}}\rbrack}}{\overset{\;}{Max}}{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}^{\;}u_{q,t}^{\prime}} ).}}} & ( {5c} )\end{matrix}$

Also in this case, the second UE numbers Upnk regardless whether iscomputed as total number, an average number, or a maximum (peak) numberof UE in the relevant surface elements 505 a-d are always dependent onthe calculated radius value Rk used to determine the relevant surfaceelements 505 a-d to which the aggregated UE numbers u_(q,t) arereferred.

The second UE numbers Upnk just computed are combined (step 618) inorder to determine an average UE number μnk

$( {{{with}\mspace{14mu} \sigma \; {nk}} = \sqrt{\frac{\sum\limits_{p = 1}^{P}( {{Upnk} - {\mu \; {nk}}} )^{2}}{P}}} )$

thus the average UE number μnk is clearly different from the second UEnumbers Upnk even if they are computed as the average number of UE inthe relevant surface elements 505 a-d) and a UE number standarddeviation μnk

$( {{{{with}\mspace{14mu} \mu \; {nk}} = {\sum\limits_{p = 1}^{P}{Upnk}}},} $

of the UE number within the relevant surface elements 505 a-d during theobservation time interval [Tsn, Ten] on the P previous days gpnconsidered.

The average UE number μnk and the UE number standard deviation σnk arecombined (step 620) with the first UE number Unk in order to obtain a(statistical) quantity defined z-score Znk (which depends on thecalculated radius value Rk):

Znk=(Unk−μnk)/σnk.  (7)

The z-score Znk just computed is compared (step 622) with a z-scorethreshold Zth and it is checked whether the z-score Znk is greater thanthe z-score threshold Zth, or:

Znk>Zth.  (8)

The z-score threshold Zth is a value preferably defined by theadministrator through the administrator interface 120 on the basis ofstatistical analysis of past public happenings of the same kind (e.g.,cultural, entertaining, politics or sport happenings).

The Applicant has found that setting the z-score threshold Zth equal to2 (i.e., Zth=2) provides good results for most kind of public happenings(although this should not construed as limitative for the presentinvention).

In the affirmative case (exit branch Y of decision block 622), i.e. thez-score Znk is greater than the z-score threshold Zth (i.e., Znk>Zth),one of the N public happenings Sn is detected and the detected number ofhappenings variable DSk is increased by unity (step 624; i.e.,DSk=DSk+1) and operation proceeds at step 626 (described hereinbelow).

In the negative case (exit branch N of decision block 622), i.e. thez-score Znk is equal to, or lower than, the z-score threshold Zth (i.e.,Znk Zth), the happening variable n is increased by unity (step 626;i.e., n=n+1).

Then (step 628), it is checked whether the happening variable n is lowerthan, or equal to, the number N of public happening Sn:

n≤N.  (9)

In the affirmative case (exit branch Y of decision block 628), i.e. thevariable n is lower than, or equal to, the number N of overall publichappenings Sn (n≤N), operation returns to step 610 for analyzing thesets {u_(q,t)} of Q aggregated UE numbers u_(q,t) referred to the publichappening Sn held on the next observation day gn.

In the negative case (exit branch N of decision block 628), i.e. thehappening variable n is greater than the number N of overall publichappenings Sn (n>N; i.e., all the N public happenings Sn have beenanalyzed), the variable k is increased by unity (step 630; i.e., k=k+1)and the calculated radius value Rk is increased (step 632):

Rk=Rmin+kΔ,  (10)

where Δ is an iteration width that may be defined by the administrator(e.g., Δ=100 m), thus each calculated radius value Rk is separated fromthe next calculated radius value by an iteration width Δ. It should benoted that the iteration width Δ define a maximum iteration value kmaxfor the iteration variable k—and, therefore, a maximum number ofiterations for determining the optimum radius value Ro—as:

kmax=(|Rmin|+Rmax)/Δ.  (11)

It should be noted that the iteration width Δ may be used by the systemadministrator to adjust a granularity (i.e., fineness) with which theoptimum radius value Ro is determined, i.e. the smaller the iterationwidth Δ set by the administrator the higher the number of iterationsdefined by the maximum iteration value kmax and, thus, the finer agranularity of the crowd estimation algorithm.

In an embodiment of the present invention, since the minimum radiusvalue Rmin is set to −1500 m, the maximum radius value Rmax is set to1500 m and the iteration width Δ is set to 100 m the maximum iterationvalue kmax for the iteration variable k results to be equal to 30 and,therefore, the maximum number of iterations for determining the optimumradius value Ro is limited to 30.

Afterwards, it is checked (step 634) whether the calculated radius valueRk is lower than, or equal to, the maximum radius value Rmax:

Rk≤Rmax.  (12)

In the affirmative case (exit branch Y of decision block 634), i.e. thecalculated radius value Rk is lower than, or equal to, the maximumradius value Rmax (i.e., Rk≤Rmax) operation returns to step 606 forstarting a new iteration of the first portion of the algorithm based onthe calculated radius value Rk just increased (at step 632) by a furtherk-th iteration width Δ.

In the negative case (exit branch N of decision block 634), i.e. thecalculated radius value Rk is greater than the maximum radius value Rmax(i.e., Rk>Rmax), the optimum radius value Ro is computed (step 636) asthe average of the computed radius values Rk (with 1≤k≤kmax) weighted bythe number DSk of detected public happening Sn within the AoI 107 havingthe AoI radius Ra equal to the same computed radius values Rk, i.e. thedetected number of happening variable DSk, or:

$\begin{matrix}{{Ro} = {\frac{\sum\limits_{k}^{\;}{{Rk} \cdot {DSk}}}{\sum\limits_{k}^{\;}{DSk}}.}} & (13)\end{matrix}$

The steps 606 to 634 of the first portion of the crowd estimationalgorithm are iterated until the calculated radius value Rk is greaterthan the maximum radius value Rmax (i.e., Rk>Rmax), and the optimumradius value Ro is computed (at step 636).

With the computation of the optimum radius value Ro at step 636 thefirst portion of the crowd estimation algorithm ends and then a secondportion of the crowd estimation algorithm starts (at step 638, describedin the following). At the end of the first portion of the crowdestimation algorithm, the AoI 107 is properly defined by the AoI centerC and by the AoI radius Ra set equal to the optimum radius value Ro(Ra=Ro).

The second portion of the crowd estimation algorithm according to anembodiment of the present invention is configured to determine a numberof persons in the crowds gathered at each one of the N public happeningsSn considered.

After the optimum radius value Ro has been computed at step 636, a setof actually relevant surface elements 505 a-d is defined (step 638).This set includes all the surface elements 205 b for which inequality(1) is verified when the AoI radius Ra is set equal to the optimumradius value Ro, or:

Dist(C,B)≤|Rs+Ro|.  (14)

Then (step 640), the happening variable n is initialized to unity (n=1)anew and all the sets {u_(q,t)} referred to the observation day gnwithin the observation time interval [Ts, Te] and having taken place inthe actually relevant surface element 505 a-d determined at step 638 areretrieved (step 642) from the repository 115.

Subsequently (step 644), a third UE number Un|_(AOI) is computed as anumber of UE comprised within the relevant surface elements 505 a-dcomprised in the AoI 107 having the AoI radius Ra equal to the optimumradius values Ro during the observation time interval [Ts, Te] on thebasis of the sets {u_(q,t)} that have been retrieved at step 642.

Similarly to the first UE number Unk, the third UE number Un|_(AOI) maybe computed as a total number, an average number, or a maximum (peak)number of UE in the relevant surface elements 505 a-d, for exampleaccording to a setting selected by the administrator of the system 100through the administrator interface 120 and/or by the user of the system100 through the user interface 125. It should be noted that, in thiscase, third UE number U|_(AOI) is computed either as a total number, anaverage number, or a maximum (peak) number of UE in the relevant surfaceelements 505 a-d within the AoI having radius Ra equal to the optimumradius value Ro, thus the third UE number is dependent on the optimumradius value Ro rather than on the calculated radius value Rk (on whichthe first UE number Unk is dependent).

Once the third UE number Un|_(AOI) has been computed, a persons numberAn is initialized to zero (i.e., An=0) (step 646). The persons number Anaccounts for the number of persons in the crowd gathered for attendingat the public happening Sn (as described in the following).

All the sets {u′_(q,t)} referred to each one of the previous days gpnwithin the observation time interval [Tsn, Ten] and having taken placein the actually relevant surface element 505 a-d determined at step 638are retrieved (step 648) from the repository 115.

Then (step 650), it is computed a fourth UE number Upn|_(AOI) for eachone of the P previous days gpn as a number of UE comprised within therelevant surface elements 505 a-d comprised in the AoI 107 having theAoI radius Ra equal to the optimum radius values Ro during theobservation time interval [Ts, Te] on the basis of the sets {u′_(q,t)}that have been retrieved at step 648.

Also in this case, similarly to the second UE numbers Upnk, each one ofthe fourth UE numbers Upn|_(AOI) may be computed as a total number, anaverage number, or a maximum (peak) number of UE in the relevant surfaceelements 505 a-d, for example according to a setting selected by theadministrator of the system 100 through the administrator interface 120and/or by the user of the system 100 through the user interface 125. Itshould be noted that, in this case, the fourth UE numbers Up|_(AOI) arecomputed either as a total number, an average number, or a maximum(peak) number of UE in the relevant surface elements 505 a-d within theAoI 107 having the AoI radius Ra equal to the optimum radius value Ro,thus the fourth UE numbers are dependent on the optimum radius value Rorather than on the calculated radius value Rk (on which the second UEnumbers Upnk are dependent).

The fourth UE numbers Upn|_(AOI) just computed are combined (step 652)in order to determine a further average UE number μn|_(AOI) of the UEnumber within the relevant surface elements 505 a-d. For example thefurther average UE number μn|_(AOI) may be computed as:

$\begin{matrix}{\mu \; n{{_{AOI}{= {\sum\limits_{p = 1}^{P}{Upn}}}}_{AOI}.}} & (15)\end{matrix}$

The further average UE number μn|_(AOI) provides an indication of anaverage number of persons normally comprised within the AoI 107 havingthe AoI radius Ra equal to the optimum radius values Ro during theconsidered observation time interval [Tsn, Ten] in any days (i.e.,people that do not gathers in the crowd).

It is pointed out that, while the further average UE number μn|_(AOI)computed as described above may provided a sort of limited accuracy(since two or more activities from a same UE within the consideredobservation time interval [Tsn, Ten] may be considered as each belongingto different UE), the further average UE number μn|_(AOI) provides anestimation of the average number of persons normally comprised withinthe AoI 107 having a satisfying accuracy provided with a lowcomputational complexity and ensuring a full respect of the privacy ofUE owners.

The person number An is then calculated (step 654) by combining (e.g.,subtracting) the further average UE number μn|_(AOI) determined at step652 from the third UE number Un|_(AOI) determined at step 644, or

An=Un| _(AOI) −μn| _(AOI).  (16)

Therefore the persons number An referred to the public happening Sn heldon the observation day gn is stored (step 656) in the repository 115,then the happening variable n is increased by unity (step 658; i.e.,n=n+1) and it is checked (step 660) whether the happening variable n islower than, or equal to, the number N of public happenings Sn (in thesame way as done at previous step 628):

n≤N.  (9)

In the affirmative case (exit branch Y of decision block 660), i.e. thehappening variable n is lower than, or equal to, the number N of publichappenings Sn (n≤N), operation returns to step 642 in order to analyzethe next public happening Sn held on the next happening day gn.

In the negative case (exit branch N of decision block 660), i.e. thehappening variable n is greater than the number N of overall publichappenings Sn (n>N), all the N public happenings Sn have been analyzedand thus the crowd estimation algorithm may be terminated.

Preferably, the algorithm is terminated by providing (step 662) theresults, i.e. the N persons number An to the user through the userterminal 125 for inspection and/or further processing.

The steps 642 to 660 of the second portion of the crowd estimationalgorithm are iterated until all the N public happenings Sn have beenanalyzed and thus the crowd estimation algorithm is terminated (at step662) with the provision of the results to the user through the userterminal 125.

In summary, the crowd estimation algorithm comprises a first portion anda second portion.

In its turn, the first portion of the crowd estimation algorithmcomprises two nested cycles. A first external cycle scans (steps606-634) all the computed radius values Rk between the minimum radiusvalue Rmin and the maximum radius value Rmax, while a first internalcycle scans (steps 610-628) all the N public happenings Sn to beanalyzed. For each computed radius value Rk respective surface element505 a-d and z-score Znk are determined. On the basis of such data (i.e.,respective relevant surface elements 505 a-d and z-score Znk) thedetected happening variable DSk is computed and the optimum radius valueRo is identified. At the end of the first portion of the crowdestimation algorithm, the AoI 107 having the optimum radius value Ro isdefined.

The second portion of the algorithm comprises a cycle that scans (steps642-660) all the N public happening Sn held within the AoI 107, anddetermines the number of persons in the crowd that attended at thepublic happening Sn.

The crowd estimation system 100 and the crowd estimation algorithmaccording to an embodiment of the present invention allows a posterioriestimation of the number of persons in a crowd attending at one or morepublic happenings Sn in a reliable way and allows properly identifying(by determining the optimum radius value Ro) an effective extension ofthe AoI 107 associated with each of the one or more public happeningsSn.

1-24. (canceled) 25: A method of estimating a number of persons thatgathered at an Area of Interest during an observation time interval on aday, wherein the Area of Interest is defined by an Area of Interestcenter and an Area of Interest radius and is covered by a mobiletelecommunication network including a plurality of communicationstations each of which is configured to manage communications of UserEquipment in one or more served areas in a covered geographic regionover which the mobile telecommunication network provides services, themethod comprising: a) defining a plurality of calculated radius valuesof the Area of Interest radius, and, for each calculated radius value:b) computing a first number of User Equipment that has been served bythe mobile communication network during the observation time interval onthe day within the Area of Interest based on aggregated data regarding ausage of the mobile communication network; c) computing a second numberof User Equipment that has been served by the mobile communicationnetwork during the observation time interval for each day of apredetermined number of previous days preceding the day within the Areaof Interest based on the aggregated data regarding the usage of themobile communication network; d) combining the first number of UserEquipment and the second numbers of User Equipment for obtaining astatistical quantity; e) detecting occurrence of a gathering of peopleif the statistical quantity reaches a certain threshold; f) computing anoptimum radius value of the Area of Interest radius as the average ofthe calculated radius values within which the gathering of people isdetected; g) estimating the number of persons that gathered within anArea of Interest having the Area of Interest radius equal to the optimumradius value. 26: The method according to claim 25, further comprisingfor each calculated radius value: h) subdividing the covered geographicregion in a plurality of surface elements; and i) receiving a pluralityof aggregated data regarding a usage of the mobile communication networkreferred to each one of the surface elements. 27: The method accordingto claim 26, wherein the i) receiving a plurality of aggregated dataregarding a usage of the mobile communication network for each one ofthe surface elements, comprises: receiving a set of aggregated data,each aggregated data of the set of the aggregated data being referred toa respective reference time interval which is a portion of anacquisition period during which aggregated data are collected. 28: Themethod according to claim 27, wherein the b) computing a first number ofUser Equipment that has been served by the mobile communication networkduring the observation time interval on the day within the Area ofInterest based on aggregated data regarding a usage of the mobilecommunication network, comprises: computing a first number of UserEquipment on the basis of sets of aggregated data referred to respectivereference time intervals comprised within the observation time intervalon the day; and wherein the c) computing a second number of UserEquipment that has been served by the mobile communication networkduring the observation time interval for each day of a predeterminednumber of previous days preceding the day within the Area of Interestbased on the aggregated data regarding the usage of the mobilecommunication network, comprises: computing each second number of UserEquipment on the basis of sets of aggregated data referred to respectivereference time intervals comprised within the observation time intervalof the respective previous day of the predetermined number of previousdays preceding the day. 29: The method according to claim 28, whereinthe first number of User Equipment and/or each second number of UserEquipment may be computed as a total number, an average number, or amaximum number of User Equipment in the relevant surface elements in theArea of Interest. 30: The method according to claim 26, furthercomprising: j) identifying a number of relevant surface elements amongthe plurality of surface elements, wherein the relevant surface elementsare at least partially superimposed on the Area of Interest. 31: Themethod according to claim 30, wherein a surface element is identified asa relevant surface element if it verifies the following condition:Dist(C,B)≤\Rs+Rk\, wherein C is the center of the Area of Interest, β isthe center of the served surface element, Dist(C, B) is the geographicaldistance between the center of the Area of Interest C and the center ofthe surface element B, Rs is the radius of the surface element, and Rkis the calculated radius value. 32: The method according to claim 25,wherein the d) combining the first number of User Equipment and thesecond numbers of User Equipment for obtaining a statistical quantitycomprises: combining the second User Equipment numbers of each one ofthe previous days to determine an average User Equipment number and aUser Equipment number standard deviation. 33: The method according toclaim 32, wherein the d) combining the first number of User Equipmentand the second numbers of User Equipment for obtaining a statisticalquantity further comprises: computing the statistical quantity as:Ink=(Unk−μnk)lσnk, wherein Unk is the first User Equipment number, μnkis the average User Equipment number, and σnk is the User Equipmentnumber standard deviation. 34: The method according to claim 25, whereinthe plurality of calculated radius values ranges from a minimum radiusvalue to a maximum radius value, each calculated radius value beingseparated from a next calculated radius value by an iteration width. 35:The method according to claim 26, wherein the g) estimating a number ofpersons that gathered in the Area of Interest having the Area ofInterest radius equal to the optimum radius value comprises: k)identifying a number of relevant surface elements among the surfaceelements subdividing the covered geographic region, wherein the relevantsurface elements are surface elements at least partially superimposed onthe Area of Interest having the Area of Interest radius equal to theoptimum radius value. 36: The method according to claim 35, wherein asurface element is identified as a relevant surface element if itverifies the following inequality:Dist(C,B)≤\Rs+Ro\, wherein C is the center of the Area of Interest, β isthe center of the served area, Dist(C, B) is the geographical distancebetween the center of the Area of Interest C and the center of thesurface element B, Rs is the radius of the surface element, and Ro isthe optimum radius value. 37: The method according to claim 35, whereinthe g) estimating a number of persons that gathered in the Area ofInterest having the Area of Interest radius equal to the optimum radiusvalue further comprises: computing a third number of User Equipment as anumber of User Equipment within the relevant surface elements in theArea of Interest having the Area of Interest radius equal to the optimumradius value during the time interval on the basis of the aggregateddata regarding a usage of the mobile communication network. 38: Themethod according to claim 37, wherein the computing a third number ofUser Equipment, comprises computing the of third number User Equipmenton the basis of sets of aggregated data referred to respective referencetime intervals comprised within the observation time interval on theday. 39: The method according to claim 34, wherein the g) estimating anumber of persons that gathered in the Area of Interest having the Areaof Interest radius equal to the optimum radius value further comprises:computing a fourth number of User Equipment as a number of UserEquipment within the relevant surface elements comprised in the Area ofInterest having the Area of Interest radius equal to the optimum radiusvalue for each day of the predetermined number of previous dayspreceding the day on the basis of the aggregated data regarding a usageof the mobile communication network. 40: The method according to claim39, wherein computing the fourth number of User Equipment, comprises:computing each fourth number of User Equipment on the basis of sets ofaggregated data referred to respective reference time intervalscomprised within the observation time interval of the respectiveprevious day of the predetermined number of previous days preceding theday. 41: The method according to claim 37, wherein the third number ofUser Equipment and/or each fourth number of User Equipment may becomputed as a total number, an average number, or a maximum number ofUser Equipment in the relevant surface elements in the Area of Interesthaving the Area of Interest radius equal to the optimum radius value.42: The method according to claim 39, wherein the g) estimating a numberof persons that gathered in the Area of Interest having the Area ofInterest radius equal to the optimum radius value further comprises:combining the fourth numbers of User Equipment of each one of theprevious days to determine a further average User Equipment number, thefurther average User Equipment number providing an indication of anaverage number of persons normally within the Area of Interest havingthe Area of Interest radius equal to the optimum radius values duringthe considered observation time interval in any days. 43: The methodaccording to claim 42, wherein the g) estimating a number of personsthat gathered in the Area of Interest having the Area of Interest radiusequal to the optimum radius value further comprises: combining the thirdnumber of User Equipment and the further average User Equipment numberto obtain the number of persons that gathered in the Area of Interesthaving the Area of Interest radius equal to the optimum radius value.44: The method according to claim 43, wherein combining the third numberof User Equipment and the further average User Equipment numbercomprises subtracting the further average User Equipment number from thethird User Equipment number. 45: The method according to claim 25,wherein the gathering of persons at an Area of Interest during anobservation time interval on a day comprises a plurality of gathering ofpersons at the Area of Interest during the observation time interval onrespective days, the method further comprising: I) iterating b) to e)for each gathering of persons; and wherein the f) computing an optimumradius value of the Area of Interest radius as the average of thecomputed radius values within which the gathering of persons isdetected, comprises: computing an optimum radius value of the Area ofInterest radius as the average of the computed radius values weighted bya number of detected gathering of persons within the Area of Interesthaving the Area of Interest radius equal to the same computed radiusvalues, the number of detected gathering of persons being the sum of thegatherings of persons determined by iterating e). 46: The methodaccording to claim 45, wherein the g) estimating a number of personsthat gathered in the Area of Interest having the Area of Interest radiusequal to the optimum radius value is iterated for each gathering ofpersons of the plurality of gathering of persons. 47: A system coupledwith a wireless telecommunication network for estimating a number ofpersons that gathered at an Area of Interest, the system comprising: acomputation engine configured to process data retrieved from a mobiletelephony network; a repository configured to store data regardinginteractions between the User Equipment and the mobile telephonynetwork, computation results generated by the computation engine and,possibly, any processing data generated by and/or provided to thesystem; an administrator interface operable for modifying parametersand/or algorithms used by the computation engine and/or accessing datastored in the repository; a memory element storing a software programproduct configured for implementing the method of claim 25 through thesystem. 48: The system according to claim 47, further comprising atleast one user interface configured to receive inputs from, and toprovide output to a user of the system, the user comprising one or morehuman beings and/or one or more external computing systems subscriber ofthe services provided by the system.